Image processing for spectral CT

ABSTRACT

A method includes estimating structure models for a voxel(s) of a spectral image based on a noise model, fitting structure models to a 3D neighborhood about the voxel(s), selecting one of the structure models for the voxel(s) based on the fittings and predetermined model selection criteria, and de-noising the voxel(s) based on the selected structure model, producing a set of de-noised spectral images. Another method includes generating a virtual contrast enhanced intermediate image for each energy image of a set of spectral images corresponding to different energy ranges based on de-noised spectral images, decomposed de-noised spectral images, an iodine map, and a contrast enhancement factor; and generating final virtual contrast enhanced images by incorporating a simulated partial volume effect with the intermediate virtual contrast enhanced images. Also described herein are approaches for generating a virtual non-contrasted image, a bone and calcification segmentation map, and an iodine map for multi-energy imaging studies.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national filing of PCT application Serial No.PCT/1B2012/053520, filed Jul. 10, 2012, published as WO 2013/011418 A2on Jan. 24, 2013, which claims the benefit of U.S. provisionalapplication Ser. No. 61/508,178 filed Jul. 15, 2011, which isincorporated herein by reference

FIELD OF THE INVENTION

The following generally relates to computed tomography (CT) and moreparticularly to spectral CT.

BACKGROUND OF THE INVENTION

A CT scanner generally includes an x-ray tube that emits ionizingradiation that traverses an examination region and a portion of anobject or subject therein and illuminates a detector array disposedacross the examination region, opposite the x-ray tube. The detectorproduces projection data indicative of the detected radiation. The datacan be reconstructed to generate volumetric image data indicative of theportion of the object or subject. With spectral CT, the projection dataincludes signals which are acquired concurrently and that correspond todifferent photon energy ranges. There are several approaches forperforming spectral CT. For example, the CT scanner may include two ormore sources, at least one source configured to switch between at leasttwo different kVps, and/or a detector array with energy-resolvingdetectors.

With spectral CT, two acquired signals can be used to determine thephotoelectric and Compton contributions of each signal and identify anunknown material by its value of photoelectric and Compton contribution.Generally, because any two linearly independent sums of two basisfunctions span the entire attenuation coefficient space, any materialcan be represented by a linear combination of two basis materials. Thisworks especially well in materials, such as iodine, that have a k-edgeenergy close to the mean value of a diagnostic energy range.Furthermore, the additional spectral information improves thequantitative information that can be determined about the scanned objectand its material composition. The basis material also allows forgenerating a monochromatic image, a material cancellation image, aneffective atomic number image, and electron density image.

Again, CT scanners emit ionizing radiation. Unfortunately, ionizingradiation may damage or kill cells and/or increase the risk of cancer.The literature has indicated that dose levels from CT typically exceedthose from conventional radiography and fluoroscopy. However, theradiation dose for a particular imaging procedure cannot just be loweredas a lower dose leads to increased image noise and thus blurrier orun-sharp image. Moreover, spectral CT images are already inherentlynoisier than conventional non-spectral images. For example, in a dualenergy study, each image is based on roughly half of the radiation doseof a corresponding non-spectral conventional scan. Furthermore, theestimate of the material decomposition is based on projections betweentwo vectors with a narrow angle there between. The combination of thesetwo factors, i.e., large noise and narrow angle, amplifies significantlythe noise in the estimated material decomposition.

Contrast enhanced CT studies capture the transit of an administeredradio-contrast material through vascular tissue. Generally, for contrastenhanced CT, a bolus of a radio-contrast material is intravenouslyadministered to a patient, and a region of interest of the patient thatincludes the vascular tissue of interest is scanned. The radio-contrastmaterial causes the x-ray density in the vascular tissue of interest totemporarily increase as the radio-contrast material flows through thevascular tissue, resulting in enhanced data. However, afteradministration of a contrast material, some patients experienceidiosyncratic effects and certain patients may experience severe andpotentially life-threatening allergic reactions. Contrast material mayalso induce kidney damage, and some patients have developed an acutedeterioration of their kidney function. Generally, a larger contrastmaterial volume results in higher contrast to noise (CNR) images, whilea lower volume leads to lower CNR image. Unfortunately, as the contrastmaterial volume increases, so does its associated risks.

SUMMARY OF THE INVENTION

Aspects of the present application address the above-referenced mattersand others.

According to one aspect, a method includes estimating a local noisevalue for one or more voxels of a spectral image of a set of spectralimages corresponding to different energy ranges, producing a noise modelfor the image, estimating local structure models for a voxel of thespectral image based on a corresponding noise model, fitting a set ofthe local structure models to a three dimensional neighborhood of voxelsin the image about a voxel in the image, selecting one of the localstructure models for the voxel based on the fittings and predeterminedmodel selection criteria, and de-noising the voxel based on the selectedlocal structure model by replacing a value of the voxel with a valueestimated based on the selected local structure model, wherein aplurality of the voxels of a plurality of spectral images in the set ofspectral images are de-noised, producing a set of de-noised spectralimages.

In another aspect, a computing apparatus includes a noise estimator thatestimates a noise pattern of a spectral image of a set of spectralimages corresponding to different energy ranges, wherein the noisepattern is used to estimate local structure models for a voxel of thespectral image, a model fitter that fits a set of the local structuremodels to a three dimensional neighborhood of voxels in the image abouta voxel in the image, and a model selector that selects one of the localstructure models for the voxel based on the fittings and predeterminedmodel selection criteria.

In another aspect, a method includes generating a calcium probabilitymap based on a probabilistic decomposition of de-noised spectral images,enhancing the calcium probability map by performing a total variationfunctional minimization of the calcium probability map and generating abinary mask representing the bone and calcium segmentation based on theenhanced calcium probability map and a predetermined threshold.

In another aspect, a method includes generating one or more iodinedistribution maps based on a vector decomposition of de-noised spectralimages and estimating an iodine map based on the one or more iodinedistribution maps and a binary mask representing the bone and calciumsegmentation.

In another aspect, a method includes generating a virtual contrastenhanced intermediate image for every energy image of a set of spectralimage corresponding to different energy ranges based on de-noisedspectral images, decomposed de-noised spectral images, an iodine map anda contrast enhancement factor, and generating final virtual contrastenhanced images by incorporating a simulated partial volume effect withthe intermediate virtual contrast enhanced images.

In another aspect, a method includes generating a virtual non-contrastintermediate image for every energy image of a set of spectral imagescorresponding to different energy ranges based on the de-noised spectralimages, decomposed de-noised spectral images and an iodine map, andgenerating final virtual non-contrast images by incorporating asimulated partial volume effect with the intermediate virtual contrastenhanced images.

Still further aspects of the present invention will be appreciated tothose of ordinary skill in the art upon reading and understanding thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 schematically illustrates an imaging system in connection with ade-noiser and an image processor.

FIG. 2 schematically illustrates an example of the de-noiser.

FIG. 3 schematically illustrates an example of a spectral noise removerof the de-noiser.

FIG. 4 schematically illustrates an example of the image processor.

FIG. 5 shows an example of an energy map/energy scatter plot of a dualenergy study and several of the material response vectors.

FIG. 6 shows two material response vectors in an energy map and theshorter distances from a measurement point to the two vectors.

FIG. 7 schematically illustrates an example segmentor of the imageprocessor.

FIG. 8 schematically illustrates an example material map generator ofthe image processor.

FIG. 9 schematically illustrates an example virtual contrast enhancedimage generator of the image processor.

FIG. 10 schematically illustrates an example virtual non-contrast imagegenerator of the image processor.

FIG. 11 illustrates an example method for de-noising spectral images.

FIG. 12 illustrates an example method for determining bone and calciumsegmentation binary mask for de-noised spectral images.

FIG. 13 illustrates an example method for determining an iodine map forde-noised spectral images.

FIG. 14 illustrates an example method for determining virtual noncontrast images based on de-noised spectral images.

FIG. 15 illustrates an example method for determining virtual contrastenhanced images based on de-noised spectral images.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 illustrates an imaging system 100 such as a computed tomography(CT) scanner configured for spectral CT imaging. The imaging system 100includes a stationary gantry 102 and a rotating gantry 104, which isrotatably supported by the stationary gantry 102. The rotating gantry104 rotates around an examination region 106 about a longitudinal orz-axis.

The system 100 includes at least one radiation source 108, such as anx-ray tube, that is supported by the rotating gantry 104 and whichrotates with the rotating gantry 104 about the examination region 106.The at least one radiation source 108 emits radiation that traverses theexamination region 106. Where there are at least two radiation sources108, each source can be configured to emit radiation having a differentmean emission spectrum. Additionally or alternatively, one or more ofthe at least two sources 108 can be configured to controllably switchbetween at least two different emission voltages (kVp's) duringscanning. Multiple sources and/or kVp switching can be used for spectralCT acquisitions.

A radiation sensitive detector array 110 is located opposite the atleast one radiation source 108, across the examination region 106. Theradiation sensitive detector array 110 includes an array of detectorpixels that detect radiation traversing the examination region 106 andgenerate projection data indicative thereof. The radiation sensitivedetector array 110 can include conventional and/or energy-resolvingdetectors such as direct conversion detectors and/or ascintillator-based multi-spectral detector that includes at least twoscintillators with different x-ray energy sensitivities respectivelyoptically affixed to at least two photosensors with correspondingoptical sensitivities (e.g., double decker or layer detector). Theenergy-resolving detectors can be used for spectral CT acquisitions.

A reconstructor 112 reconstructs the projection data and generatesvolumetric image data indicative of the examination region 106 and theportion of the object or subject therein. Where spectral data isacquired (e.g., where the projection data includes at least twomeasurements acquired concurrently and corresponding to different energyranges via multiple sources, kVp switching and/or energy-resolvingdetectors), the reconstructor 112 can reconstruct individual spectralimages for each of the different energy ranges and/or combination imagesbased on the individual spectral images corresponding to two or more ofthe different energy ranges. The reconstructor 112 can also employconventional non-spectral reconstruction algorithms.

A subject support 114, such as a couch, supports a subject (e.g., ahuman or animal) or object in the examination region 106 and can be usedto position the subject with respect to x, y, and/or z axes and theexamination 106 before, during and/or after scanning. A general purposecomputing system serves as an operator console 116, and includes anoutput device such as a display and an input device such as a keyboard,mouse, and/or the like. Software resident on the console 116 allows theoperator to control the operation of the system 100, for example,allowing the operator to select a spectral imaging protocol, initiatescanning, etc.

A computing apparatus 118 includes one or more processors that executeone or more computer executable instructions embedded or encoded oncomputer readable storage medium such as physical memory. Additionallyor alternatively, one or more of the computer executable instructionscan be carried by a signal or carrier wave and executed by the one ormore processors. In the illustrated embodiment, the computer executableinstructions include instructions for implementing a de-noiser 120and/or an image processor 122. In another embodiment, the de-noiser 120and/or the image processor 122 are implemented via the console 116and/or other device.

The de-noiser 120 is configured to de-noise spectral noise fromreconstructed spectral images, removing or reducing spectral noisetherefrom and producing de-noised reconstructed spectral images. Asdescribed in greater detail below, in one instance, the de-noiser 120removes or reduces spectral noise while preserving the underlyingspectral information and the object structure. In one instance, thisallows radiation dose reduction for a given image quality.Alternatively, image quality can be enhanced for a given dose.Alternatively, a combination of dose reduction and image qualityenhanced can be achieved. Additionally or alternatively, the de-noiser120 can de-noise estimated monochromatic images, which can be simulatedto estimate any keV image using an appropriate combination ofphotoelectric and Compton components.

An image processor 122 processes the de-noised reconstructed spectralimages and/or the monochromatic images. As described in greater detailbelow, this includes one or more of performing a bone and/or calciumsegmentation, creating an iodine map of a quantitative distribution ofthe iodine in a study, generating virtual contrast enhanced (VCE)images, and/or generating virtual non-contrast (VNC) images. Such boneand calcification segmentation can highly utilize the additionalquantitative spectral information, be utilized within a beam hardeningcorrection algorithm, be utilized with a monochromatic imagereconstruction algorithm, etc. The iodine map provides an improvedquantitative distribution of the iodine in the study.

Virtually enhancing contrast allows for reducing the amount of contrastmaterial administered to a patient for a given image quality.Alternatively, it allows for saving a study where the scanning timingfrom administration has been missed and the resulting image hassuboptimal image quality, which may result in a repeat scan and furthercontrast material. Alternatively, it allows a clinician to manuallytweak image processing parameters via a mouse, keyboard or the like toprobe images in real time and obtain a desired visualization result. TheVNC image may eliminate the need for a non-contrast scan, which candecrease radiation exposure, save time, and prolong tube life.

The computing apparatus 118 also includes a user interface 124, whichallows a user to interact with the computing apparatus 118. In oneinstance, this includes allowing a clinician to choose which of theabove-noted image processing features (i.e., bone and calciumsegmentation, iodine map generation, VNC image generation and/or VCEimage generation) to employ for a given study. The user interface 124also allows the clinician to set and/or change various image processingparameters. For example, the clinician can use the user interface 124 tochange the amount of de-noising for the study. This can be donedynamically in real time with the results being presented in real time.

That is, the clinician, viewing results, can change a parameter that inresponse invokes the computing apparatus to process the de-noisedreconstructed spectral images based on the changed parameter andvisually present the results. Other parameters which may be userconfigurable include, but are not limited to, a contrast enhancementfactor for VCE image processing, parameters affecting the aggressivenessof the simulate partial volume effect for VCE and VNC image generation,thresholds for selecting de-local structural models for de-noising,scaling factors for bone and calcium segmentation, weighting factors forfitting local structure models to voxels, etc.

A data repository 126 can be used to store the reconstructed images,de-noised reconstructed images and/or processed reconstructed imagesand/or de-noised reconstructed images and can be accessed by one or moreof the console 116, the image processor 122, the de-noiser 120 and/orother device. The data repository 126 can be local to the system 100,remote from the system 100, distributed, etc. The data repository 126may include a database, a server, a picture archiving and communicationsystem (PACS), radiology information system (RIS), a hospitalinformation system (HIS), an electronic medical record (EMR), and/orother electronic storage device or memory.

FIG. 2 schematically illustrates an example of the de-noiser 120.Generally, in this embodiment, the de-noiser 120 is configured todetermine a noise pattern for a spectral image in a set of spectralimages corresponding to different energy ranges and reduce spectralnoise of the spectral images based on the noise pattern. The illustratedde-noiser 120 receives as input a set of spectral images, which couldinclude a set of reconstructed spectral images from the scanner 100, therepository 126 and/or elsewhere, and/or a set of estimated monochromaticimages.

A noise estimator 202 estimates a local noise value of each voxel of aspectral image and generates a noise model or pattern for the spectralimage based on the local noise values, and the noise model is used toestimate structures in the spectral image. The noise estimator 202 canuse known and/or other approaches to estimate noise. Suitable approachesinclude, but are not limited to, a Monte Carlo estimate, an analyticalapproach such as the one discussed in Wunderlich and Noo, Phys. Med.Biol. 53 (2008), 2472-2493, an image based approach such as the onedescribed in PCT patent application serial number PCT/IB2009/054913,filed on Oct. 29, 2010, and entitled “ENHANCED IMAGE DATA/DOSEREDUCTION,” which is incorporated by reference in its entirety herein,and/or other approach.

A spectral noise remover 204 removes spectral image noise from thespectral images based on the estimated noise model, generating de-noisedspectral images, while preserving underlying spectral information and/oranatomical structure in the different energy images, thereby improvingthe signal to noise ratio of the spectral images. An example of this isdescribed in connection with FIG. 3, where the spectral noise remover204 includes a model fitter 302 that fits local structure models, whichare determined based on the estimated noise pattern, to a threedimensional region or neighborhood of voxels about a voxel, for one ormore of the voxels in the spectral image.

The spectral noise remover 204 also includes a model selector 304 thatselects a local structure model for each voxel in an image based onpredetermined selection criteria stored in criteria memory 306. Once amodel is selected for each voxel, it is utilized by the spectral noiseremover 204 to de-noise or remove the noise in the spectral images,where a new estimated value of a voxel is a value determined by theselected model and replaces the original value of the voxel. Theresulting spectral images include de-noised spectral images, or imagequality enhanced spectral image for the different energy ranges.

With reference to FIGS. 1, 2 and 3, an example noise removal approachfor the de-noiser 120 follows. For this example, v_(i,j,k) ^(E) ^(e)represents a voxel in the volume V^(E) ^(e) , where the volume obtainedby energy E_(e). EQUATION 1 includes a least squares approach that canbe used to fit the local structure models:

$\begin{matrix}{{{\hat{p}}^{Ee} = {\underset{p}{argmin}{\sum\limits_{i^{\prime} = {- n}}^{n}\;{\sum\limits_{j^{\prime} = {- n}}^{n}\;{\sum\limits_{k^{\prime} = {- n}}^{n}\;{\left( {v_{{i + i^{\prime}},{j + j^{\prime}},{k + k^{\prime}}}^{Ee} - {M_{i^{\prime},j^{\prime},k^{\prime}}^{m}(p)}} \right)^{2}w_{i^{\prime},j^{\prime},k^{\prime}}^{2}}}}}}},} & {{EQUATION}\mspace{14mu} 1}\end{matrix}$where M_(i′,j′,k′) ^(m)(p) is the model value for the (i+i′,j+j′, k+k′)voxel in the volume and W_(i′,j′,k′) are weight factors. The weightfactors can be considered a localization kernel, which is amultiplication of two weight functions as shown in EQUATION 2:W _(i′,j′,k′) =W ^(spatial) _(i′,j′,k′) W ^(HU) _(i′,j′,k′,)  EQUATION2:where W^(spatial) _(i′,j′,k′) represents weights to the neighborsaccording to their spatial distance to the voxel and W^(HU) _(i′,j′,k′)represents weights to the neighbors according to theirintensity-distance to the voxel in the Hounsfield Unit (HU) space.

The W^(spatial) _(i′,j′,k′) function can be determined based on EQUATION3:

$\begin{matrix}{{w_{i^{\prime},j^{\prime},k^{\prime}}^{spatial} = \sqrt{\exp\left( {- \frac{\left( {\left( {i^{\prime}{dx}} \right)^{2} + \left( {j^{\prime}{dx}} \right)^{2} + \left( {k^{\prime}{dz}} \right)^{2}} \right)}{2\sigma_{spatial}^{2}}} \right)}},} & {{EQUATION}\mspace{14mu} 3}\end{matrix}$where dx is the size of the pixel in millimeters (mm), dz is the slicewidth in mm and σ_(spatial) is an algorithm parameter that controls theaggressiveness of the weights.The W^(HU) _(i′,j′,k′) function can be determined based on EQUATION 4:

$\begin{matrix}{w_{i^{\prime},j^{\prime},k^{\prime}}^{HU} = \sqrt{\exp\left( {- {\sum\limits_{e}\;\frac{\left( {v_{i,j,k}^{E_{e}} - v_{{i + i^{\prime}},{j + j^{\prime}},{k + k^{\prime}}}^{E_{e}}} \right)^{2}}{2\left( {{\hat{n}}_{i,j,k}^{E_{e}}m} \right)^{2}}}} \right)}} & {{EQUATION}\mspace{14mu} 4}\end{matrix}$where m is an algorithm parameter that controls the aggressiveness ofthe weights and {circumflex over (n)}_(i,j,k) is the local noise levelestimate of voxel v_(i,j,k), which is estimated by the noise estimator202 as described above.

Suitable models include, but are not limited to, a constant model (i.e.,M_(i′,j′,k′)(c)=c) that models homogeneous regions and a second orderpolynomial that models the non-homogeneous regions (i.e., regions thatincludes curvatures). Other models can additionally or alternatively beused. The model selector 304 can use various known and/or otherclassifiers to select a suitable model. In this example, the illustratedmodel selector 304 utilizes INEQUALITY 1:

$\begin{matrix}{{\underset{e}{Max}\frac{{local}\mspace{14mu}{STD}\mspace{14mu}{in}\mspace{14mu}\left( {i,j,k} \right)\mspace{14mu}{over}\mspace{14mu}{\hat{V}}_{1}^{E_{e}}}{{local}\mspace{14mu}{STD}\mspace{14mu}{in}\mspace{14mu}\left( {i,j,k} \right)\mspace{14mu}{over}\mspace{14mu} V^{E_{e}}}} > {{Threshold}.}} & {{INEQUALITY}\mspace{14mu} 1}\end{matrix}$where {circumflex over (V)}₁ ^(E) ^(e) is the noiseless estimated voxelof the first model and threshold corresponds to criteria stored incriteria memory 306. In this example, if INEQUALITY 1 is satisfied, thenoise removal is performed using the second model parameter.

Once a model is chosen, the spectral noise remover 204 uses the model tode-noise the spectral images, thereby generating de-noised spectralimages. In one instance, the same noise model type and the same fittingweights are used for all the different energy images. This allowsremoval of noise while preserving consistent results in the spectralimages across the different energy ranges. In another instance,different noise model types and/or the fitting weights are used for oneor more of the different energy images.

FIG. 4 schematically illustrates an example of the image processor 120.In this embodiment, the image processor 120 includes a material analyzer402, decomposition algorithm memory 404, a segmentor 406, a mapgenerator 408, a virtual contrast enhanced (VCE) image generator 410,and a virtual non-contrast (VNC) image generator 412.

The material analyzer 402 decomposes the spectral images de-noised bythe de-noiser 118 and/or other de-noised spectral images according todifferent material bases as each material has a unique attenuationspectral response, i.e., each material has a unique material responsevector on an image-based energy map. A dual energy scanner is able todistinguish, in principal, between tissues or material of variabledensity with greater resolving power than a conventional CT scanner.This is shown in connection with FIG. 5 which shows an example of anenergy map/energy scatter plot of a dual energy study and several of thematerial response vectors.

The material analyzer 402 decomposes the de-noised reconstructedspectral images being analyzed based on various known and/or otherdecomposition algorithms such as one or more decomposition algorithmsstored in the decomposition algorithm memory 404. Examples ofnon-limiting decomposition algorithms are further discussed next. Onesuitable decomposition algorithm is based on a vector decompositionapproach. For example, the material analyzer 402 can estimate materialdistribution maps by solving the linear equations of EQUATION 5:

$\begin{matrix}{{\sum\limits_{m = 1}^{n}\;{\overset{\rightarrow}{M_{m}}\alpha_{i,j,k}^{m}}} = \overset{\rightarrow}{\begin{bmatrix}{\hat{v}}_{i,j,k}^{1} \\\vdots \\{\hat{v}}_{i,j,k}^{n}\end{bmatrix}}} & {{EQUATION}\mspace{14mu} 5}\end{matrix}$where {right arrow over (M_(m))} is a material vector related tomaterial m, {circumflex over (V)}^(e) is the volume related to energy eobtained after de-noising via the de-noiser 118, n is the number ofenergy bins and α^(m) is an estimated material distribution map relatedto material m. Another suitable decomposition algorithm is based on aprobabilistic decomposition approach. For example, the material analyzer402 can estimate material distribution maps based of EQUATION 6:

$\begin{matrix}{{\left\{ {\hat{\alpha}}_{m} \right\} = {{\underset{{\{\alpha_{m}\}},{{\sum\limits_{m}\;\alpha_{m}} = 1}}{argmax}{\sum\limits_{i}\;{\log{\sum\limits_{m = 1}^{n}\;{{f\left( p_{i} \middle| M_{m} \right)}\alpha_{m}}}}}} = {\underset{{\{\alpha_{m}\}},{{\sum\limits_{m}\;\alpha_{m}} = 1}}{argmax}{\overset{n}{\sum\limits_{i = 1}}\;{\log{\sum\limits_{m = 1}^{n}\;{{f\left( d_{i,m} \middle| M_{m} \right)}\alpha_{m}}}}}}}},} & {{EQUATION}\mspace{14mu} 6}\end{matrix}$where f (p_(i)|M_(m)) is the probability density function of a pointp_(i) to contain material M_(m) and d_(i,m) is the shortest distance inthe energy map between point p_(i) to the material vector related tomaterial M_(m). This can be seen in FIG. 6, which shows two materialresponse vectors 602 and 604 in an energy map 606 and the shorterdistances 608 and 610 from a measurements point p_(i) 612 to the twovectors 602 and 604.

The material probability map estimate can be determined based ofEQUATION 7:

$\begin{matrix}{{{P_{m}\left( {v_{i}:=M_{m}} \right)} = \frac{{f\left( v_{i} \middle| {io} \right)}\alpha_{m}}{\sum\limits_{m}\;{{f\left( v_{i} \middle| {io} \right)}\alpha_{m}}}},} & {{EQUATION}\mspace{14mu} 7}\end{matrix}$where P_(m) is the material probability map of material M_(m).Generally, this approach uses the distribution of the distances in theenergy map of the voxels to the material response vectors as aprobability mixture model of several materials. The output of thisdecomposition includes material probability maps that represent theprobability of each voxel including specific material.

Returning to FIG. 4 and with reference to FIG. 7, the segmentor 406 isconfigured to at least segment bone and calcifications based ondecomposed de-noise spectral images.

As shown in FIG. 7, the segmentor 406 receives as input a calciumprobability map P_(m), which is estimated by the material analyzer 402based on calcium, iodine and soft tissue and/or other materials, usingthe probabilistic material decomposition algorithm (EQUATIONS 6 and 7)from the memory 404.

An enhancer 704 enhances the probabilistic material decomposition usinga total variation functional minimization or other approach. In thisexample, the enhancer 702 performs a total variation functionalminimization based on EQUATION 8:

$\begin{matrix}{{\hat{u} = {{\underset{u}{Min}\underset{i,j,k}{\int{\int\int}}{{\nabla u}}} + {\lambda\underset{i,j,k}{\int{\int\int}}\left( {{P_{m}\left( {i,j,k} \right)} - {u\left( {i,j,k} \right)}} \right)^{2}{\mathbb{d}i}{\mathbb{d}j}{\mathbb{d}k}}}},} & {{EQUATION}\mspace{14mu} 8}\end{matrix}$where λ, is a positive parameter that controls the scale of thesegmentation solution. The parameter λ, can be a default or userspecified value. Various approaches can be used to solve EQUATION 8. Anon-limiting approach can be found in Tony F. Chan, Jianhong Shen, ImageProcessing and Analysis, SIAM Books 2005.

The segmentor 406 further includes a mask estimator 704 that estimatesan image binary map, which represents the segmentation of bone andcalcification. In this example, the mask estimator 706 generates the mapB based on EQUATION 9:B=û>Threshold.  EQUATION 9:

As noted above, the resulting bone and calcification segmentation canhighly utilize the additional quantitative spectral information, beutilized within a beam hardening correction algorithm, be utilized witha monochromatic image reconstruction algorithm, etc.

Returning to FIG. 4 and with reference to FIG. 8, the illustrated mapgenerator 408 is configured to generate an iodine map. Generally, themap generator 408 is configured to estimate an iodine map based ondecomposed de-noised spectral images and the bone and calcium mask. Forthis example, the iodine map incorporates calcium, fat, soft tissue, andiodine. In other embodiment, more, less and/or different materials canbe used.

As shown in FIG. 8, the map generator 408 receives as input iodinedistribution maps generated based on the vector decomposition (EQUATION5) of the material analyzer 402 and the bone and calcification map fromthe segmentor 406. In this dual energy example, the map generator 408receives a first iodine distribution map, α_(Soft) ^(Iodine), based oniodine and soft tissue and a second iodine distribution map α_(Fat)^(Iodine), based on iodine and fat. In instances with three or moredifferent energy ranges, a single iodine map for iodine, soft tissue andfat can be generated and/or more iodine maps can be generated.

An iodine map estimator 802 estimates an iodine map, IM, based onEQUATION 10:

$\begin{matrix}{{{IM}\left( {i,j,k} \right)} = \left\{ \begin{matrix}{0,} & {B\left( {i,j,k} \right)} \\{{{\alpha_{Fat}^{Iodine}\left( {i,j,k} \right)}q},} & \begin{matrix}{{{\alpha_{Fat}^{Iodine}\left( {i,j,k} \right)}} <} \\{{{{\alpha_{Soft}^{Iodine}\left( {i,j,k} \right)}}\bigwedge{\hat{v}}_{i,j,k}^{e}} < {0{\forall e}}}\end{matrix} \\{{{\alpha_{Soft}^{Iodine}\left( {i,j,k} \right)}q},} & {{Otherwise},}\end{matrix} \right.} & {{EQUATION}\mspace{14mu} 10}\end{matrix}$where q is a constant scale factor that is dependent on the requiredquantitative unit. As noted above, the resulting iodine map IM providesan improved quantitative distribution of the iodine in the study.

Returning to FIG. 4 and with reference to FIG. 9, the illustrated VCEimage generator 410 is configured to compensate for a reduction ofcontrast material by virtual enhancement of the spectral images. The VCEimage generator 410 receives as input the de-noised spectral images, thedecomposed de-noise spectral images, and the iodine map generated by themap generator 408.

An intermediate VCE 902 generates, for every energy, e, a preliminaryVCE image based on EQUATION 11:

$\begin{matrix}{{{vt}_{i,j,k}^{E_{e}} = {v_{i,j,k}^{E_{e}} + {\gamma\frac{{\overset{\rightarrow}{M}}_{Iodine}(e)}{{\overset{\rightarrow}{M}}_{Iodine}}{IM}_{i,j,k}}}},} & {{EQUATION}\mspace{14mu} 11}\end{matrix}$where γ is the enhancement factor. In one instance, γ=1/X−1 in order tocompensate for a contrast material volume reduction by factor of x. Inother instances, γ can be a different value such as a default or userspecified value. A final VCE image estimator 904 estimates a final imagebased on the intermediate image and a simulated partial volume effectbased on EQUATION 12:

$\begin{matrix}{{vce}_{i,j,k}^{E_{e}} = {{\frac{{{{\nabla v_{i,j,k}^{E_{e}}}}\beta} + \delta}{{{{\nabla{vt}_{i,j,k}^{E_{e}}}}\beta} + \delta}{vt}_{i,j,k}} + {\left( {1 - \frac{{{{\nabla v_{i,j,k}^{E_{e}}}}\beta} + \delta}{{{{\nabla{vt}_{i,j,k}^{E_{e}}}}\beta} + \delta}} \right){{{LPF}\left( {vt}_{i,j,k}^{E_{e}} \right)}.}}}} & {{EQUATION}\mspace{14mu} 12}\end{matrix}$where LPF is a low pass filter over the image and β and δ are parametersthat control an aggressiveness of the simulated partial volume effect.

As noted above, virtually enhancing contrast allows for reducing theamount of contrast material administered to a patient for a given imagequality. Alternatively, it allows for saving a study where the scanningtiming from administration has been missed and the resulting image hassuboptimal image quality, which may result in a repeat scan and furthercontrast material. Alternatively, it allows a clinician to manuallytweak image processing parameters via a mouse, keyboard or the like toprobe images in real time and obtain a desired visualization result.

Returning to FIG. 4 and with reference to FIG. 10, the VNC imagegenerator 412 is configured to estimate VNC images. The VNC imagegenerator 412 receives as input the decomposed data generated by thematerial analyzer 402 and the iodine map IM generated by the mapgenerator 408.

An intermediate VNC image generator 1002 generates, for every energy, e,a preliminary VNC image as follow based on EQUATION 13:

$\begin{matrix}{{vp}_{i,j,k}^{E_{e}} = {v_{i,j,k}^{E_{e}} - {\frac{{\overset{\rightarrow}{M}}_{Iodine}(e)}{{\overset{\rightarrow}{M}}_{Iodine}}{{IM}_{i,j,k}.}}}} & {{EQUATION}\mspace{14mu} 13}\end{matrix}$A final VNC image estimator 1004 estimates a final image based on theintermediate image and the simulated partial volume effect based onEQUATION 14:

$\begin{matrix}{{vnc}_{i,j,k}^{E_{e}} = {{\frac{{{{\nabla v_{i,j,k}^{E_{e}}}}\beta} + \delta}{{{{\nabla{vp}_{i,j,k}^{E_{e}}}}\beta} + \delta}{vp}_{i,j,k}^{E_{e}}} + {\left( {1 - \frac{{{{\nabla v_{i,j,k}^{E_{e}}}}\beta} + \delta}{{{{\nabla{vp}_{i,j,k}^{E_{e}}}}\beta} + \delta}} \right){{{LPF}\left( {vp}_{i,j,k}^{E_{e}} \right)}.}}}} & {{EQUATION}\mspace{14mu} 14}\end{matrix}$where LPF is a low pass filter over the image and β and δ are parametersthat control the aggressiveness of the simulated partial volume effect.The VNC image may eliminate the need for a non-contrast scan, which candecrease radiation exposure, save time, and prolong tube life.

FIGS. 11, 12, 13, 14 and 15 illustrate various methods for processing aset of reconstructed spectral CT images and/or a set of estimatedmonochromatic images.

It is to be appreciated that the ordering of the below acts is forexplanatory purposes and not limiting. As such, other orderings are alsocontemplated herein. In addition, one or more of the acts may be omittedand/or one or more other acts may be included.

Initially referring to FIG. 11, an example method for de-noising thespectral images is illustrated.

At 1102, a set of spectral images are obtained.

At 1104, a noise model is estimated for a spectral image of the set ofspectral images.

At 1106, structures in the image are estimated based on the noise model,producing local structure models.

At 1108, a set of the local structure models corresponding to a voxel inthe image are fitted to a three dimensional neighborhood of voxels aboutthe voxel.

At 1110, a structure model from the set of local structure models isselected for the voxel based on the fits and predetermined selectioncriteria.

At 1112, the voxel is de-noised based on the selected model, wherein avalue of the voxel is replaced with a value determined by the selectedmodel.

The above can be repeated for one or more other voxels of one or more ofthe other spectral images, producing de-noised spectral images for thedifferent energy ranges.

Turning to FIG. 12, an example method for generating a bone and calciumsegmentation for the spectral images is illustrated.

At 1202, a calcium probability map is generated based on a probabilisticdecomposition of the de-noised spectral images.

At 1204, the calcium probability map is enhanced by performing a totalvariation functional variation minimization of the calcium probabilitymap.

At 1206, a binary map representing the bone and calcium segmentation isdetermined based on the enhanced calcium probability map and apredetermined threshold.

Next, FIG. 13 illustrates an example method for generating an iodine mapfor the spectral images.

At 1302, one or more iodine distribution maps are generated based on avector decomposition of the de-noised spectral images.

At 1304, a bone and calcium segmentation binary mask is generated, forexample, as described in connection with FIG. 12.

At 1306, an iodine map is estimated based on the iodine distributionmaps and the bone and calcium segmentation binary mask.

In FIG. 14, an example method for generating virtual non-contrast (VNC)images for the spectral images is illustrated.

At 1402, an iodine map is estimated, for example, as described inconnection with FIG. 13.

At 1404, intermediate VNC images are estimated for every energy based onthe de-noised images, vector decomposed de-noised images, and the iodinemap.

At 1406, final VNC images are generated by incorporating a simulatedpartial volume effect with the intermediate VNC images.

FIG. 15 illustrates an example method for generating virtual contrastenhanced (VCE) images for the spectral images is illustrated.

At 1502, an iodine map is estimated, for example, as described inconnection with FIG. 13.

At 1504, a contrast enhancement factor is obtained.

At 1506, intermediate VCE images are estimated for every energy based onthe de-noised images, vector decomposed de-noised images, the iodinemap, and the contrast enhancement factor.

At 1508, final VCE images are generated by incorporating a simulatedpartial volume effect with the intermediate VCE images.

The above may be implemented via one or more processors executing one ormore computer readable instructions encoded or embodied on computerreadable storage medium such as physical memory which causes the one ormore processors to carry out the various acts and/or other functionsand/or acts. Additionally or alternatively, the one or more processorscan execute instructions carried by transitory medium such as a signalor carrier wave.

The invention has been described herein with reference to the variousembodiments. Modifications and alterations may occur to others uponreading the description herein. It is intended that the invention beconstrued as including all such modifications and alterations insofar asthey come within the scope of the appended claims or the equivalentsthereof.

What is claimed is:
 1. A method, comprising: estimating a local noisevalue for one or more voxels of a spectral image of a set of spectralimages corresponding to different energy ranges, producing a noise modelfor the spectral image; estimating local structure models for a voxel ofthe spectral image based on a corresponding noise model; selecting oneof the local structure models for the voxel of the spectral image basedon predetermined model selection criteria; and de-noising the voxel ofeach spectral image of the set of spectral images based on the selectedlocal structure model by replacing a value of the voxel of each spectralimage with a value estimated based on the selected local structuremodel, wherein a plurality of the voxels of a plurality of spectralimages in the set of spectral images are de-noised, producing a set ofde-noised spectral images.
 2. The method of claim 1, further comprising,before the selecting of the one of the local structure models for thevoxel based on predetermined model selection criteria: fitting a set ofthe local structure models to a three dimensional neighborhood of voxelsin the spectral image of the set of spectral images about a voxel in thespectral image of the set of spectral images; and selecting the one ofthe local structure models for the voxel based on the fittings and thepredetermined model selection criteria.
 3. The method of claim 1,wherein the set of spectral images are generated with data acquiredduring a first dose scan, and the de-noising of the set spectral imagesproduces the set of de-noised spectral images that include an amount ofimage noise that is at a same level of image noise as for a second setof spectral images generated with data acquired during a second dosescan, wherein a dose of the second dose scan is higher than a dose ofthe first dose scan.
 4. The method of claim 1, further comprising: usinga least squares minimization to fit the models; and weighting the leastsquares minimization with a weighting factor, wherein the weight factorincludes a first weighting component that weights the three dimensionalneighborhood of voxels about the voxel based on a voxel intensitydistance between the neighboring voxels and the voxel.
 5. The method ofclaim 4, wherein the first weighting component is a function of thelocal noise value of the voxel of the spectral image.
 6. The method ofclaim 4, wherein the weight factor weights include a second weightingcomponent that weights the three dimensional neighborhood about thevoxel of the spectral image based on a spatial distance between theneighboring voxels and the voxel of the spectral image.
 7. The method ofclaim 1, wherein the local structure models include at least two noisemodels, wherein the at least two local structure models include at leasta constant model that models homogenous regions and a second orderpolynomial that models non-homogeneous regions.
 8. The method of claim6, further comprising: selecting a local structure model of the fittednoise models for de-noising the voxel of the spectral image based on apredetermined relationship between a ratio of a local standarddeviations of the at least two local structure models and apredetermined threshold.
 9. The method of claim 1, further comprising:generating a calcium probability map based on a probabilisticdecomposition of the set of de-noised spectral images; enhancing thecalcium probability map by performing a total variation functionalvariation minimization of the calcium probability map; and generating abinary mask representing the bone and calcium segmentation based on theenhanced calcium probability map and a predetermined threshold.
 10. Themethod of claim 9, further comprising: generating one or more iodinedistribution maps based on a vector decomposition of the set ofde-noised spectral images; and estimating an iodine map based on the oneor more iodine distribution maps and the binary mask.
 11. The method ofclaim 10, further comprising: generating a virtual contrast enhancedintermediate image for each of the different energy ranges based on thede-noised spectral images, decomposed de-noised spectral images, theiodine map, and a contrast enhancement factor; and generating finalvirtual contrast enhanced images by incorporating a simulated partialvolume effect with the intermediate virtual contrast enhanced images.12. The method of claim 11, wherein the contrast enhancement factor hasa value that compensates for a reduction in contrast material.
 13. Themethod of claim 10, further comprising: generating a virtualnon-contrast intermediate image for each of the different energy rangesbased on the set of de-noised spectral images, decomposed de-noisedspectral images, and the iodine map; and generating final virtualnon-contrast images by incorporating a simulated partial volume effectwith the intermediate virtual contrast enhanced images.
 14. A computingapparatus, comprising: a noise estimator that includes one or moreprocessors configured to estimate a noise pattern of a spectral image ofa set of spectral images corresponding to different energy ranges,wherein the noise pattern is used to estimate local structure models fora voxel of the spectral image; and a model selector that includes theone or more processors configured to select of the local structuremodels for the voxel of the spectral image based on predetermined modelselection criteria; and a model fitter that includes the one or moreprocessors configured to fit a set of the local structure models to athree dimensional neighborhood of voxels in the image about a voxel inthe spectral image, wherein the model selector selects the one of thelocal structure models for the voxel based on the fittings and thepredetermined model selection criteria.
 15. The computing apparatus ofclaim 14, further comprising: a spectral noise remover that includes theone or more processors configured to de-noise the voxel of each spectralimage of the set of spectral images based on the selected localstructure model by replacing a value of the voxel of each spectral imagewith a value estimated based on the selected local structure model,wherein a plurality of voxels of a plurality of spectral images in theset of spectral images are de-noised, producing a set of de-noisedspectral images.
 16. The computing apparatus of claim 15, furthercomprising: a material analyzer that includes the one or more processorsconfigured to generate a calcium probability map based on aprobabilistic decomposition of the set of de-noised spectral images; anenhancer that includes the one or more processors configured to enhancethe calcium probability map by performing a total variation functionalvariation minimization of the calcium probability map; and a maskestimator that includes the one or more processors configured togenerate binary mask representing the bone and calcium segmentationbased on the enhanced calcium probability map and a predeterminedthreshold.
 17. The computing apparatus of claim 16, wherein the materialanalyzer generates one or more iodine distribution maps based on avector decomposition of the set of de-noised spectral images and,further comprising: an iodine map estimator that includes the one ormore processors configured to estimate an iodine map based on the one ormore iodine distribution maps and the binary mask.
 18. The computingapparatus of claim 17, further comprising: an intermediate virtualcontrast enhanced image generator that includes the one or moreprocessors configured to generate a virtual contrast enhancedintermediate image for each of the different energy ranges based on theset of de-noised spectral images, decomposed de-noised spectral images,the iodine map, and a contrast enhancement factor; and a final virtualcontrast enhanced image estimator that generates final virtual contrastenhanced images by incorporating a simulated partial volume effect withthe intermediate virtual contrast enhanced images.
 19. The computingapparatus of claim 17, further comprising: an intermediate virtual noncontrast image generator that includes the one or more processorsconfigured to generate a virtual non contrast intermediate image foreach of the different energy ranges based on the de-noised spectralimages, decomposed de-noised spectral images, and the iodine map; and afinal virtual non contrast image generator that includes the one or moreprocessors configured to generate final virtual non contrast images byincorporating a simulated partial volume effect with the intermediatevirtual non contrast images.